Multiple Sampling for Estimation on a Finite Horizon

dc.contributor.advisorBaras, John S.en_US
dc.contributor.authorRabi, Mabenen_US
dc.contributor.authorBaras, John S.en_US
dc.contributor.authorMoustakides, Georgeen_US
dc.contributor.departmentISRen_US
dc.contributor.departmentSEILen_US
dc.date.accessioned2007-05-23T10:18:29Z
dc.date.available2007-05-23T10:18:29Z
dc.date.issued2006en_US
dc.description.abstractWe discuss some multiple sampling problems that arise in real-time estimation problems with limits on the number of samples. The quality of estimation is measured by an aggregate squared error over a finite horizon. We compare the performances of the best detereministic, level-triggered and the optimal sampling schemes. We restrict the signal to be either a Wiener process or an Ornstein-Uhlenbeck process. For the Wiener process, we provide closed form expressions and series expansions. For the Ornstein Uhlenbeck process, we provide procedures for numerical computation. Our results show that level-triggered sampling is almost optimal when the signal is stable. <p>en_US
dc.format.extent99410 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6586
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 2006-7en_US
dc.relation.ispartofseriesSEIL; TR 2006-2en_US
dc.subjectSensor-Actuator Networksen_US
dc.titleMultiple Sampling for Estimation on a Finite Horizonen_US
dc.typeTechnical Reporten_US

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